Advanced Modeling and Simulation in Engineering Sciences

Scope & Guideline

To Transform Engineering with Advanced Simulation Techniques.

Introduction

Welcome to the Advanced Modeling and Simulation in Engineering Sciences information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Advanced Modeling and Simulation in Engineering Sciences, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN-
PublisherSPRINGERNATURE
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationADV MODEL SIMUL ENG / Adv. Model. Simul. Eng. Sci.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressCAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

Aims and Scopes

The journal 'Advanced Modeling and Simulation in Engineering Sciences' focuses on the development and application of advanced computational techniques and modeling approaches in engineering sciences. It aims to bridge the gap between theoretical modeling, numerical simulation, and practical engineering applications.
  1. Computational Mechanics and Engineering:
    The journal emphasizes the use of computational methods to address complex engineering problems, including structural dynamics, fluid mechanics, and thermomechanical systems.
  2. Machine Learning and Data-Driven Approaches:
    A significant focus is placed on integrating machine learning techniques with traditional modeling approaches to enhance predictive capabilities, optimize designs, and improve model accuracy.
  3. Multiscale and Multiphysics Modeling:
    The journal covers research that involves the interaction of different physical phenomena at various scales, allowing for a comprehensive understanding of complex systems.
  4. Model Order Reduction Techniques:
    There is a consistent emphasis on developing and applying model order reduction techniques to simplify complex simulations while maintaining essential characteristics of the original models.
  5. Physics-Informed Neural Networks:
    The incorporation of physics-informed neural networks represents a unique contribution, merging data-driven methods with established physical laws for improved modeling accuracy.
Recent publications in 'Advanced Modeling and Simulation in Engineering Sciences' reveal emerging themes that reflect the evolving landscape of engineering research. These trends indicate a growing interest in innovative methodologies and applications.
  1. Integration of Machine Learning with Simulation:
    There is a marked increase in research that combines machine learning techniques with simulation methods, enhancing predictive accuracy and enabling real-time applications.
  2. Advanced Surrogate Modeling Techniques:
    Emerging approaches in surrogate modeling, particularly those that leverage deep learning and data-driven methods, are becoming prominent for reducing computational costs in complex simulations.
  3. Hybrid Modeling Approaches:
    The trend towards hybrid models that integrate both data-driven and physics-based approaches is gaining traction, allowing for more robust and adaptable modeling frameworks.
  4. Application of Graph Neural Networks:
    The application of graph neural networks in various engineering contexts is emerging as a significant trend, particularly for complex systems where traditional methods may struggle.
  5. Focus on Uncertainty Quantification:
    There is an increasing emphasis on uncertainty quantification in modeling and simulations, reflecting the importance of understanding and managing uncertainties in engineering applications.

Declining or Waning

While certain themes remain prominent, some areas of research have shown a decline in focus within recent publications of the journal. These waning scopes suggest a shift in research priorities or methodologies.
  1. Traditional Finite Element Methods without Enhancements:
    There is a noticeable decrease in papers solely focused on conventional finite element methods, indicating a shift towards more advanced and hybrid approaches that integrate machine learning and other computational techniques.
  2. Basic Theoretical Frameworks:
    Research that relies heavily on basic theoretical frameworks without application to complex problems is becoming less frequent, as the field moves towards more applied and interdisciplinary studies.
  3. Simplistic Modeling Approaches:
    As the demand for precision and complexity in simulations increases, simpler modeling approaches that do not account for multiphysics or multiscale interactions are declining.
  4. Uncoupled Simulations:
    The trend towards integrated and coupled simulations has led to a decline in the publication of studies that focus on uncoupled simulations, reflecting a broader interest in comprehensive system modeling.

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